An Introduction to Fintech Data Science

Data science is an interdisciplinary field dealing with scientific methods, systems and processes and includes extracting insights or knowledge from data in different forms. It involves data mining, visualization, machine learning, databases, cluster analysis, classification among others. Data science has given rise to what is now known as fintech (financial technology). The impact of data science in the financial sector is evident in predictive analytics and big data both of which have improved accuracy in financial decision making. For the better understanding of fintech data science, we bring you some of its most common applications below.

Risk Assessment

Risk assessment is of great significance in the finance sector. Whether you are making investment decisions or creating an online working capital, you will need to conduct a risk assessment. Fintech data science has revolutionized risk assessment by making credit scoring much easier and more accurate than before. An online capital lender can quite accurately determine the creditworthiness of a borrower by analyzing thousands of data points. As a result, credit risk is substantially diminished.

Customer Transaction Habits

Data science is critical in the holistic analysis of customer behaviour across multiple channels of engagement. A financial institution is able to classify payment records, therefore, enabling it to have customer-tailored services. For instance, integration of personal data and payment records makes it possible for a financial institution to implement proactive customer interfacing strategies such as loyalty rewards and target marketing.

Asset Management

Data science has made it possible for institutions to make use of large volumes of data in building asset management models that have enabled them to obtain high risk adjusted returns for customers. The creation of robor-advisors that are geared towards individual investors is one of the major breakthroughs in the fintech sector, thanks to data science. Robo-advisors take into account historical trends and data points therefore making sound and scientific asset allocation decisions regarding investable assets.

Customer Journey Attribution

Customer acquisition and lifetime value are very important to fintech companies, and indeed for any financial service provider. Application of fintech data science enables firms to evaluate the potential lifetime purchase volume for every customer. This enables the firm to classify customers in a model for the purposes of upselling and targeted marketing. The application of fintech data science makes it possible to make use of diverse metrics such as social media feeds and direct feedback obtained through surveys to come up with a lifetime value model.

Fraud Detection and Prevention

Fraud detection and prevention are very important in the financial services sector. The sector deals with sensitive customer data such as credit cards and passwords which if compromised would occasion considerable loss. Data science enables fintech firms to make real-time assessment of any given payment to find out whether it has a fraudulent motive.